Exploring text and image features to classify images in bioscience literature

Rafkind, Barry; Lee, Minsuk; Chang, Shih-Fu; Yang, Hong

doi:10.3115/1654415.1654428

Cited by 20 publications

(23 citation statements)

References 1 publication

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“…As part of our work for the Elsevier Challenge, we expanded the classification to other panel types. This mirrors other systems that have appeared since the original SLIF which include more panel types [7,8,9].…”

Section: Image Processingmentioning

confidence: 93%

Structured Literature Image Finder: Parsing Text and Figures in Biomedical Literature

et al. 2010

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The SLIF project combines text-mining and image processing to extract structured information from biomedical literature.SLIF extracts images and their captions from published papers. The captions are automatically parsed for relevant biological entities (protein and cell type names), while the images are classified according to their type (e.g., micrograph or gel). Fluorescence microscopy images are further processed and classified according to the depicted subcellular localization.The results of this process can be queried online using either a user-friendly web-interface or an XML-based web-service. As an alternative to the targeted query paradigm, SLIF also supports browsing the collection based on latent topic models which are derived from both the annotated text and the image data.The SLIF web application, as well as labeled datasets used for training system components, is publicly available at http://slif.cbi.cmu.edu.

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Section: Image Processingmentioning

confidence: 93%

Structured Literature Image Finder: Parsing Text and Figures in Biomedical Literature

et al. 2010

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“…Other efforts explored biomedical article retrieval based on image content [10,11], and use of textual and image features for image classification in biomedical articles [12]. While preliminary studies on image-only retrieval have resulted in mediocre results, classification of bioscience images into six generic categories achieved an average F-score of 73.66% [12].…”

Section: Introductionmentioning

confidence: 99%

Automatically Finding Images for Clinical Decision Support

Emara

Karnstedt

Sattler³

et al. 2007

Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)

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Essential information is often conveyed in illustrations in biomedical publications. A clinician's decision to access the full text when searching for evidence in support of clinical decision is frequently based solely on a short bibliographic reference. We seek to automatically augment these references with images from the article that may assist in finding evidence.The feasibility of automatically classifying images by usefulness (utility) in finding evidence was explored using supervised machine learning. We selected 2004 --2005 issues of the British Journal of Oral and Maxillofacial Surgery, manually annotating 743 images by utility and modality (radiological, photo, etc.) Image data, figure captions, and paragraphs surrounding figure discussions in text were used in classification.Automatic image classification achieved 84.3% accuracy using image captions for modality and 76.6% accuracy combining captions and image data for utility.Our results indicate that automatic augmentation of bibliographic references with relevant images is feasible.

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“…The most similar study [6] is a fusion classifier to classify images in biological literature. The classifier is constructed on top of SVM classifiers trained on image and text features.…”

Section: Introductionmentioning

confidence: 99%

“…The automated identification of FMIs is therefore a crucial step in SLIF. Recently, other systems for classifying biological journal figures have been described [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Identifying Fluorescence Microscope Images in Online Journal Articles Using Both Image and Text Features

Hua

Ayasli²,

Cohen³

et al. 2007

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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We have previously built a Subcelluar Location Image Finder (SLIF) system, which extracts information regarding protein subcellular location patterns from both text and images in journal articles. One important task in SLIF is to identify fluorescence microscope images. To improve the performance of this binary classification problem, a set of 7 edge features extracted from images and a set of "bag of words" text features extracted from text have been introduced in addition to the 64 intensity histogram features we have used previously. An overall accuracy of 88.6% has been achieved with an SVM classifier. A co-training algorithm has also been applied to the problem to utilize the unlabeled dataset and it substantially increases the accuracy when the training set is very small but can contribute very little when the training set is large.

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Exploring text and image features to classify images in bioscience literature

Cited by 20 publications

References 1 publication

Structured Literature Image Finder: Parsing Text and Figures in Biomedical Literature

Structured Literature Image Finder: Parsing Text and Figures in Biomedical Literature

Automatically Finding Images for Clinical Decision Support

Identifying Fluorescence Microscope Images in Online Journal Articles Using Both Image and Text Features

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